人工智能驱动的分子吸附预测 (AIMAP) 应用于金属上氨基酸吸附的性识别
Zi-Xing Guo1, Guo-Liang Song1, Zhi-Pan Liu1,2,3
1Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China guoliangsong@fudan.edu.cn zpliu@fudan.edu.cn.
Chemical science
|August 26, 2024
概括
预测分子吸附是复杂的. 一个人工智能工具AIMAP使用全球神经网络和优化来预测吸附结构,创建一个大数据库并揭示铜表面的氨基酸相互作用.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 表面科学是一门学科.
背景情况:
- 由于复杂的表面结合和分子灵活性,预测分子吸附结构具有挑战性.
- 准确的预测对于理解表面化学和设计新材料至关重要.
研究的目的:
- 开发一种人工智能驱动的分子吸附预测 (AIMAP) 工具,用于对分子吸附结构的通用端到端预测.
- 为了有效地探索吸附系统的全球潜在能量表面.
主要方法:
- 利用全球神经网络 (G-NN) 的潜力,有效地探索潜在能量表面.
- 采用随机表面行走 (SSW) 全球优化,用于快速选和吸附模式的精细搜索.
- 建立了一个全面的Cu-HCNO6吸附数据库,在三个铜表面上为9592个分子提供超过110万个配置.
主要成果:
- AIMAP成功地预测了 18 种氨基酸 (AAs) 在 achiral 和 chiral 铜表面上的吸附结构.
- 确定AAs在最高无质子状态下对铜进行化学吸收,涉及骨和侧面组.
- 观察到性识别,在性铜表面上对d-和l-氨基酸有特殊偏好.
结论:
- AIMAP为预测分子吸附结构提供了一种高效且通用的工具.
- 氨基酸在铜表面的吸附是复杂的,涉及通过各种分子部分的脱质和相互作用.
- 这项研究合理化了因子特异性吸附的物理起源,突出了在低协调位点竞争性吸附的作用.
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